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Synthesis of Sparse Planar Arrays With Multiple Patterns by the Generalized Matrix Enhancement and Matrix Pencil

机译:广义矩阵增强和矩阵铅笔具有多种图案的稀疏平面阵列的合成

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摘要

In recent years, the matrix enhancement and matrix pencil method (MEMP) as well as the forward-backward MEMP (FBMEMP) have been successfully applied to reduce the number of antenna elements in the single-pattern planar arrays. This article aims to extend the MEMP and FBMEMP to the synthesis of sparse planar arrays with multiple patterns. The generalized MEMP (GMEMP) first constructs an enhanced matrix for each target pattern according to their data samples, respectively, and then these enhanced matrices are used to form a composite Hankel block matrix. After that, two sets of poles corresponding to two sets of reconstructed location coordinates can be extracted from the principal eigenvectors of the composite Hankel block matrix by the matrix pencil method. The randomized singular value decomposition and Hankel matrix decomposition method are used to accelerate the above extraction process. Then, we make use of improved matching algorithm to pair the two sets of position coordinates to obtain 2-D coordinates. Finally, we use the least-square method to obtain the excitations of sparse planar array elements, which makes the reconstruction process of multiple patterns more stable and accurate. Apart from the above, the GMEMP is also extended to the synthesis of multiple shaped-beam patterns using the generalized forward-backward matrix enhancement and matrix pencil method (GFBMEMP). A series of representative numerical examples show that the proposed methods can reduce the number of elements in the planar arrays efficiently while maintaining the accuracy of all the patterns to be synthesized.
机译:近年来,已经成功地应用了矩阵增强和矩阵铅笔方法(MEMP)以及前后MEMP(FBMEMP)以减少单图案平面阵列中的天线元件的数量。本文旨在将MEMP和FBMEMP扩展到具有多种模式的稀疏平面阵列的合成。广义MEMP(GMEMP)首先根据其数据样本为每个目标图案构建增强矩阵,然后这些增强矩阵用于形成复合嗜烟块矩阵。之后,可以通过矩阵铅笔方法从复合Hankel块矩阵的主要特征向量中提取对应于两组重建位置坐标的两组极。随机奇异值分解和Hankel基质分解方法用于加速上述提取过程。然后,我们利用改进的匹配算法将两组位置坐标配对以获得2-D坐标。最后,我们使用最小二乘法来获得稀疏平面阵列元件的激励,这使得多种模式的重建过程更稳定和准确。除了以上,GMEMP还使用广义前后矩阵增强和矩阵铅笔方法(GFBMEMP)扩展到多重梁模式的合成。一系列代表性的数值示例表明,该方法可以有效地减少平面阵列中的元件数量,同时保持所合成的所有图案的精度。

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